A Comprehensive Review of Computer Vision Techniques to Interest Physicians and Surgeons, Role of A Clinical Biomechanical Engineer in Pre-Operative Surgical Planning, And Preamble To HSG-Amoeba, A New Concept of Biomedical Image Modeling Technique.
DOI:
https://doi.org/10.24297/ijct.v22i.9219Keywords:
Sternotomy, Segementation, Active contour, Convolutional neural network, solid modelling, Patient-appropriate medicineAbstract
Background: The science of computer vision is replication of human vision for pattern recognition and segregation of objects-of-interest at macro- and micro-level. There are numerous computer vision techniques with greater focus on deep learning utilizing artificial neural network. Only few of them can be readily applied to medical images for surgical interventions.
Study objective: As this narrative review is aimed at the medical community it is not encumbered with mathematical algorithms, albeit important. Apart from discussion on basic concepts and chronological development of the computer vision techniques the study introduces role of clinical biomechanical engineering team at the time of surgical planning.
Methodology: The study literature was searched on Google Scholar, keywords on Google chrome, Wikipedia and cited references in the reviewed articles referring to the original studies describing various computer vision techniques between 1980 to 2021.
Result: There is enormous discursive literature to read with extremely variable computer vision terminology unknown to the medical community is densely populated with advanced mathematics leading to lack of interest among majority of the physicians as the end user. There are inconsistencies in the usage of medical terminology and definitions.
Comments and conclusion: Standalone image processing and segmentation is meaningless without patient information for clinical applications in daily practice. There is a dire need for streamlining of computer vision science to teach medical community, introduction of a new breed of in-house clinical biomechanical engineers and supplementary residency program for residents to accept it as standard of patient care.
Downloads
References
Aggarwal, R., Sounderajah, V., Martin, G., Ting, D. S. W., Karthikesalingam, A., King, D., Ashrafian, H., & Darzi, A. (2021). Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis. In npj Digital Medicine (Vol. 4, Issue 1). Nature Research. https://doi.org/10.1038/s41746-021-00438-z
Alan I. Penn, M. H. L. (1996, April 16). Estimating fractal dimension of medical images. Proceedings SPIE Volume 2710, Medical Imaging 1996: Image Processing; (1996).
Aljabar, P., Heckemann, R. A., Hammers, A., Hajnal, J. V., & Rueckert, D. (2009). Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy. NeuroImage. https://doi.org/10.1016/j.neuroimage.2009.02.018
Anaya-Isaza, A., Mera-Jiménez, L., & Zequera-Diaz, M. (2021). An overview of deep learning in medical imaging. In Informatics in Medicine Unlocked (Vol. 26). Elsevier Ltd. https://doi.org/10.1016/j.imu.2021.100723
Barten, P. G. J. (1992). Physical model for the contrast sensitivity of the human eye. Human Vision, Visual Processing, and Digital Display III. https://doi.org/10.1117/12.135956
Bathe, K. J. (2006). Finite element procedures. Second edition. In Mit.
Benoit B. Mandelbrot. (1984). The fractal geometry of Nature. The American Mathematical Monthly, 91(9), 594–598.
Bertrand, S., Laporte, S., Parent, S., Skalli, W., & Mitton, D. (2008). Three-dimensional reconstruction of the rib cage from biplanar radiography. IRBM. https://doi.org/10.1016/j.rbmret.2008.03.005
Blanz, V., & Vetter, T. (1999). A morphable model for the synthesis of 3D faces. Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 1999. https://doi.org/10.1145/311535.311556
Blanz, V., & Vetter, T. (2003). Face recognition based on fitting a 3D morphable model. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/TPAMI.2003.1227983
The evolution of intelligence. The nervous system as a model of its environment, Technical report, no. 1, contract no. 477(17).
Candemir, S., Jaeger, S., Antani, S., Bagci, U., Folio, L. R., Xu, Z., & Thoma, G. (2016). Atlas-based rib-bone detection in chest X-rays. Computerized Medical Imaging and Graphics. https://doi.org/10.1016/j.compmedimag.2016.04.002
Canny, J. (1986). A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/TPAMI.1986.4767851
Castleman, K. R. (1996). Digital image processing (Secnond). Prentice Hall Inc.
Chan, T., & Vese, L. (1999). An active contour model without edges. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
Chang, Q., Qu, H., Zhang, Y., Sabuncu, M., Chen, C., Zhang, T., & Metaxas, D. (2020). Synthetic Learning: Learn From Distributed Asynchronized Discriminator GAN Without Sharing Medical Image Data. http://arxiv.org/abs/2006.00080
Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., & Ronneberger, O. (2016). 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. http://arxiv.org/abs/1606.06650
Coates, A., Huval, B., Wang, T., Wu, D. J., Ng, A. Y., & Catanzaro, B. (2013). Deep learning with COTS HPC systems.
Cohen, L. D. (1991). On active contour models and balloons. CVGIP: Image Understanding. https://doi.org/10.1016/1049-9660(91)90028-N
Continuum Mechanics for Engineers, Third Edition. (2009). In Continuum Mechanics for Engineers, Third Edition. https://doi.org/10.1201/9781420085396
Cootes, T. F., Edwards, G. J., & Taylor, C. J. (1998). Active appearance models. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). https://doi.org/10.1007/BFb0054760
Cootes, T. F., & Taylor, C. J. (2002). Using grey-level models to improve active shape model search. https://doi.org/10.1109/icpr.1994.576227
Cootes, T. F., Taylor, C. J., Cooper, D. H., & Graham, J. (1992). Training Models of Shape from Sets of Examples. In BMVC92. https://doi.org/10.1007/978-1-4471-3201-1_2
Cootes, T. F., Taylor, C. J., Cooper, D. H., & Graham, J. (1995). Active shape models - their training and application. Computer Vision and Image Understanding. https://doi.org/10.1006/cviu.1995.1004
da Silva, L. G., da Silva Monteiro, W. R. S., de Aguiar Moreira, T. M., Rabelo, M. A. E., de Assis, E. A. C. P., & de Souza, G. T. (2021). Fractal dimension analysis as an easy computational approach to improve breast cancer histopathological diagnosis. Applied Microscopy, 51(1). https://doi.org/10.1186/s42649-021-00055-w
Davies, B. E. R., & Holloway, R. (2005). Machine Vision : Theory , Algorithms and Practicalities , Third Edition Order from Morgan Kaufmann Publishers. Pattern Recognition Letters.
Del Toro, O. A. J., Goksel, O., Menze, B., Müller, H., Langs, G., Weber, M. A., Eggel, I., Gruenberg, K., Holzer, M., Jakab, A., Kontokotsios, G., Krenn, M., Fernandez, T. S., Schaer, R., Taha, A. A., Winterstein, M., & Hanbury, A. (2014). VISCERAL -VISual concept extraction challenge in RAdioLogy: ISBI 2014 challenge organization. CEUR Workshop Proceedings.
Duan, Y., & Qin, H. (2001). Intelligent Balloon: A subdivision-based deformable model for surface reconstruction of arbitrary topology. Proceedings of the Symposium on Solid Modeling and Applications.
Dzung, L., Chenyang, X., & Prince, J. L. (1998). A survey of current methods in medical image segmentation. Department of ECE, Johns Hopkins Univ., Tech. Rep. https://doi.org/10.1039/C5AN01075F
Gage Canadian dictionary. (1983). Gage educational publishing company.
Gandhi, H. S. (2019). Rationale and options for choosing an optimal closure technique for primary midsagittal osteochondrotomy of the sternum. Part 3: Technical decision making based on the practice of patient- appropriate medicine. Critical Reviews in Biomedical Engineering. https://doi.org/10.1615/CritRevBiomedEng.2019026454
Gibson, S. F. F., & Mirtich, B. (1997). A Survey of Deformable Modeling in Computer Graphics. Merl - a Mitsubishi Electric Research Laboratory.
Goldberg, D. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman Publishing Co., Inc.
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative Adversarial Networks. http://arxiv.org/abs/1406.2661
Gower, J. C. (1975). Generalized procrustes analysis. Psychometrika. https://doi.org/10.1007/BF02291478
Haddon, J. F. (1988). Generalised threshold selection for edge detection. Pattern Recognition. https://doi.org/10.1016/0031-3203(88)90054-4
Hamameh, G., McFnemey, T., & Terzopoulos, D. (2001). Deformable organisms for automatic medical image analysis. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). https://doi.org/10.1007/3-540-45468-3_9
Hamarneh, G., Abu-Gharbieh, R., & Mcinerney, T. (2004). Medial profiles for modeling deformation and statistical analysis of shape and their use in medical image segmentation. International Journal of Shape Modeling. https://doi.org/10.1142/S0218654304000663
Hamarneh, G., McIntosh, C., McInerney, T., & Terzopoulos, D. (2009). Deformable organisms: An artificial life framework for automated medical image analysis. In Computational Intelligence in Medical Imaging: Techniques and Applications. https://doi.org/10.1201/9781420060614
Hamarneh, G., Ward, A. D., & Frank, R. (2007). Quantification and visualization of localized and intuitive shape variability using a novel medial-based shape representation. 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings. https://doi.org/10.1109/ISBI.2007.357081
Heimann, T., & Meinzer, H. P. (2009). Statistical shape models for 3D medical image segmentation: A review. Medical Image Analysis. https://doi.org/10.1016/j.media.2009.05.004
Holland, J. H. (1962). Outline for a Logical Theory of Adaptive Systems. Journal of the ACM (JACM). https://doi.org/10.1145/321127.321128
Hubel, D. H., & Wiesel, T. N. (1962). Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. The Journal of Physiology. https://doi.org/10.1113/jphysiol.1962.sp006837
Jolliffe, I. T. (2002). Principal Component Analysis, Second Edition. Encyclopedia of Statistics in Behavioral Science. https://doi.org/10.2307/1270093
Jones, M. J., & Poggio, T. (1998). Multidimensional Morphable Models: A Framework for Representing and Matching Object Classes. International Journal of Computer Vision. https://doi.org/10.1023/A:1008074226832
Kass, M., Witkin, A., & Terzopoulos, D. (1988). Snakes: Active contour models. International Journal of Computer Vision. https://doi.org/10.1007/BF00133570
Kenji Suzuki. (2017). Overview of deep learning in medical imaging. Radiol Phys Technol, 10(3), 257–273.
Kenyon, C. M., Pedley, T. J., & Higenbottam, T. W. (1991). Adaptive modeling of the human rib cage in median sternotomy. Journal of Applied Physiology, 70(5), 2287–2302. https://doi.org/10.1152/jappl.1991.70.5.2287
Khosravan, N., Mortazi, A., Wallace, M., & Bagci, U. (2019). PAN: Projective Adversarial Network for Medical Image Segmentation. http://arxiv.org/abs/1906.04378
Klette, R. (2014). Concise Computer Vision - An Introduction into Theory and Algorithms. Springer-Verlag.
Kokash, N. (2005). An introduction to heuristic algorithms. Department of Informatics and Telecommunications.
Lecun, Y., Bengio, Y., & Hinton, G. (2015a). Deep learning. In Nature. https://doi.org/10.1038/nature14539
Lecun, Y., Bengio, Y., & Hinton, G. (2015b). Deep learning. In Nature (Vol. 521, Issue 7553, pp. 436–444). Nature Publishing Group. https://doi.org/10.1038/nature14539
Li, C., Liu, J., & Fox, M. D. (2005). Segmentation of edge preserving gradient vector flow: An approach toward automatically initializing and splitting of snakes. Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005. https://doi.org/10.1109/CVPR.2005.314
Liu, X., Song, L., Liu, S., & Zhang, Y. (2021). A review of deep-learning-based medical image segmentation methods. Sustainability (Switzerland), 13(3), 1–29. https://doi.org/10.3390/su13031224
Long, J., Shelhamer, E., & Darrell, T. (2014). Fully Convolutional Networks for Semantic Segmentation. http://arxiv.org/abs/1411.4038
Luke, S. (2013). Essentials of Metaheuristics, second edition. In Optimization.
Lyon, R. F. (2006). A brief history of “pixel.” Digital Photography II. https://doi.org/10.1117/12.644941
Ma, W. Y., & Manjunath, B. S. (2000). EdgeFlow: a technique for boundary detection and image segmentation. IEEE Transactions on Image Processing. https://doi.org/10.1109/83.855433
Malladi, R., Sethian, J. A., & Vemuri, B. C. (1995). Shape Modeling with Front Propagation: A Level Set Approach. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/34.368173
Mandar, M., Sontakke, D., Meghana, M., & Kulkarni, S. (2015). Different Types of Noises in Images and Noise Removing Technique. International Journal of Advanced Technology in Engineering and Science.
Marr, D., & Hildreth, E. (1980). Theory of edge detection. Proceedings of the Royal Society of London Series B, Containing Papers of a Biological Character Royal Society (Great Britain).
Marusina, M. Y., Mochalina, A. P., Frolova, E. P., Satikov, V. I., Barchuk, A. A., Kuznetcov, V. I., Gaidukov, V. S., & Tarakanov, S. A. (2017). MRI image processing based on fractal analysis. Asian Pacific Journal of Cancer Prevention, 18(1), 51–55. https://doi.org/10.22034/APJCP.2017.18.1.51
McInerney, T., Hamarneh, G., & Shenton, M Terzopoulos, D. (2002). Deformable organisms for automatic medical image analysis. Med Image Anal., 6(3), 251–266.
McInerney, T., & Terzopoulos, D. (1996). Deformable models in medical image analysis: A survey. Medical Image Analysis. https://doi.org/10.1016/S1361-8415(96)80007-7
Mendoza, F. & Lu, R. (2015). Basics of image analysis. In B. & L. R. Park (Ed.), Hyperspectral imaging technology in food and agriculture (pp. 9–56). Springer Science + Business Media.
Milletari, F., Navab, N., & Ahmadi, S.-A. (n.d.). V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. http://promise12.grand-challenge.org/results/
Mumford, D., & Shah, J. (1989). Optimal approximations by piecewise smooth functions and associated variational problems. Communications on Pure and Applied Mathematics. https://doi.org/10.1002/cpa.3160420503
Nealen, A., Müller, M., Keiser, R., Boxerman, E., & Carlson, M. (2006). Physically based deformable models in computer graphics. Computer Graphics Forum. https://doi.org/10.1111/j.1467-8659.2006.01000.x
Olabarriaga, S. D., & Smeulders, A. W. M. (2001). Interaction in the segmentation of medical images: A survey. Medical Image Analysis. https://doi.org/10.1016/S1361-8415(00)00041-4
Osher, S., & Sethian, J. A. (1988). Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations. Journal of Computational Physics. https://doi.org/10.1016/0021-9991(88)90002-2
Otsu, N. (1979). THRESHOLD SELECTION METHOD FROM GRAY-LEVEL HISTOGRAMS. IEEE Trans Syst Man Cybern. https://doi.org/10.1109/tsmc.1979.4310076
Petrie, R. J., & Yamada, K. M. (2016). Multiple mechanisms of 3D migration: The origins of plasticity. In Current Opinion in Cell Biology. https://doi.org/10.1016/j.ceb.2016.03.025
Pham, D. L., Xu, C., & Prince, J. L. (2000). A survey in current methods in medical image processing. Annual Review of Biomedical Engineering. https://doi.org/10.1146/annurev.bioeng.2.1.315
Prats-Montalbán, J. M., de Juan, A., & Ferrer, A. (2011). Multivariate image analysis: A review with applications. In Chemometrics and Intelligent Laboratory Systems. https://doi.org/10.1016/j.chemolab.2011.03.002
Preim, B., & Botha, C. (2014). Image Analysis for Medical Visualization. In Visual Computing for Medicine. https://doi.org/10.1016/b978-0-12-415873-3.00004-3
Prewitt, J. M. S., & Mendelsohn, M. L. (1966). THE ANALYSIS OF CELL IMAGES. Annals of the New York Academy of Sciences. https://doi.org/10.1111/j.1749-6632.1965.tb11715.x
Requicha, A. A. G., & Voelcker, H. B. (1982). Solid Modeling: A Historical Summary and Contemporary Assessment. IEEE Computer Graphics and Applications. https://doi.org/10.1109/MCG.1982.1674149
Rizwan I Haque, I., & Neubert, J. (2020). Deep learning approaches to biomedical image segmentation. In Informatics in Medicine Unlocked (Vol. 18). Elsevier Ltd. https://doi.org/10.1016/j.imu.2020.100297
Rodriguez, J., Choudhury, A. A., & Dragone, B. (2011). Application of parametric solid modeling for orthopedic studies of the human spine. ASEE Annual Conference and Exposition, Conference Proceedings.
Romanycia, M. H. J., & Pelletier, F. J. (1985). What is a heuristic? Computational Intelligence. https://doi.org/10.1111/j.1467-8640.1985.tb00058.x
Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. http://arxiv.org/abs/1505.04597
Sabuncu, M. R., Balci, S. K., Golland, P., Shenton, M. E., Shenton, M. E., & Shenton, M. E. (2009). Image-Driven Population Analysis Through Mixture Modeling. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/TMI.2009.2017942
Sahoo, P. K., Soltani, S., & Wong, A. K. C. (1988). A survey of thresholding techniques. In Computer Vision, Graphics and Image Processing. https://doi.org/10.1016/0734-189X(88)90022-9
Salvi, J., Matabosch, C., Fofi, D., & Forest, J. (2007). A review of recent range image registration methods with accuracy evaluation. Image and Vision Computing. https://doi.org/10.1016/j.imavis.2006.05.012
Senthilkumaran, N., & Rajesh, R. (2009). Edge Detection Techniques for Image Segmentation – A Survey of Soft Computing Approaches. International Journal of Recent Trends in Engineering.
Sethian, J. A. (1985). Curvature and the evolution of fronts. Communications in Mathematical Physics. https://doi.org/10.1007/BF01210742
Shah, J. (1996). Common framework for curve evolution, segmentation and anisotropic diffusion. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
Shotton, J., Winn, J., Rother, C., & Criminisi, A. (2009). Texton Boost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context. International Journal of Computer Vision. https://doi.org/10.1007/s11263-007-0109-1
Sotiras, A., Davatzikos, C., & Paragios, N. (2013). Deformable medical image registration: A survey. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/TMI.2013.2265603
Sperling, G. (1970). Binocular Vision: A Physical and a Neural Theory. The American Journal of Psychology. https://doi.org/10.2307/1420686
Tam, G. K. L., Cheng, Z. Q., Lai, Y. K., Langbein, F. C., Liu, Y., Marshall, D., Martin, R. R., Sun, X. F., & Rosin, P. L. (2013). Registration of 3d point clouds and meshes: A survey from rigid to Nonrigid. In IEEE Transactions on Visualization and Computer Graphics. https://doi.org/10.1109/TVCG.2012.310
Terzopoulos, D. (1986). Regularization of Inverse Visual Problems Involving Discontinuities. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/TPAMI.1986.4767807
Timothy F. Cootes, Gareth J. Edwards, and C. J. T. (2001). Active appearance models. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 23(6), 681–685.
Vala, M., & Baxi, A. (2013). A review on Otsu image segmentation algorithm. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET).
van Kaick, O., Zhang, H., Hamarneh, G., & Cohen-Or, D. (2011). A survey on shape correspondence. Eurographics Symposium on Geometry Processing. https://doi.org/10.1111/j.1467-8659.2011.01884.x
Aggarwal, R., Sounderajah, V., Martin, G., Ting, D. S. W., Karthikesalingam, A., King, D., Ashrafian, H., & Darzi, A. (2021). Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis. In npj Digital Medicine (Vol. 4, Issue 1). Nature Research. https://doi.org/10.1038/s41746-021-00438-z
Alan I. Penn, M. H. L. (1996, April 16). Estimating fractal dimension of medical images. Proceedings SPIE Volume 2710, Medical Imaging 1996: Image Processing; (1996).
Aljabar, P., Heckemann, R. A., Hammers, A., Hajnal, J. V., & Rueckert, D. (2009). Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy. NeuroImage. https://doi.org/10.1016/j.neuroimage.2009.02.018
Anaya-Isaza, A., Mera-Jiménez, L., & Zequera-Diaz, M. (2021). An overview of deep learning in medical imaging. In Informatics in Medicine Unlocked (Vol. 26). Elsevier Ltd. https://doi.org/10.1016/j.imu.2021.100723
Barten, P. G. J. (1992). Physical model for the contrast sensitivity of the human eye. Human Vision, Visual Processing, and Digital Display III. https://doi.org/10.1117/12.135956
Bathe, K. J. (2006). Finite element procedures. Second edition. In Mit.
Benoit B. Mandelbrot. (1984). The fractal geometry of Nature. The American Mathematical Monthly, 91(9), 594–598.
Bertrand, S., Laporte, S., Parent, S., Skalli, W., & Mitton, D. (2008). Three-dimensional reconstruction of the rib cage from biplanar radiography. IRBM. https://doi.org/10.1016/j.rbmret.2008.03.005
Blanz, V., & Vetter, T. (1999). A morphable model for the synthesis of 3D faces. Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 1999. https://doi.org/10.1145/311535.311556
Blanz, V., & Vetter, T. (2003). Face recognition based on fitting a 3D morphable model. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/TPAMI.2003.1227983
The evolution of intelligence. The nervous system as a model of its environment, Technical report, no. 1, contract no. 477(17).
Candemir, S., Jaeger, S., Antani, S., Bagci, U., Folio, L. R., Xu, Z., & Thoma, G. (2016). Atlas-based rib-bone detection in chest X-rays. Computerized Medical Imaging and Graphics. https://doi.org/10.1016/j.compmedimag.2016.04.002
Canny, J. (1986). A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/TPAMI.1986.4767851
Castleman, K. R. (1996). Digital image processing (Secnond). Prentice Hall Inc.
Chan, T., & Vese, L. (1999). An active contour model without edges. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
Chang, Q., Qu, H., Zhang, Y., Sabuncu, M., Chen, C., Zhang, T., & Metaxas, D. (2020). Synthetic Learning: Learn From Distributed Asynchronized Discriminator GAN Without Sharing Medical Image Data. http://arxiv.org/abs/2006.00080
Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., & Ronneberger, O. (2016). 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. http://arxiv.org/abs/1606.06650
Coates, A., Huval, B., Wang, T., Wu, D. J., Ng, A. Y., & Catanzaro, B. (2013). Deep learning with COTS HPC systems.
Cohen, L. D. (1991). On active contour models and balloons. CVGIP: Image Understanding. https://doi.org/10.1016/1049-9660(91)90028-N
Continuum Mechanics for Engineers, Third Edition. (2009). In Continuum Mechanics for Engineers, Third Edition. https://doi.org/10.1201/9781420085396
Cootes, T. F., Edwards, G. J., & Taylor, C. J. (1998). Active appearance models. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). https://doi.org/10.1007/BFb0054760
Cootes, T. F., & Taylor, C. J. (2002). Using grey-level models to improve active shape model search. https://doi.org/10.1109/icpr.1994.576227
Cootes, T. F., Taylor, C. J., Cooper, D. H., & Graham, J. (1992). Training Models of Shape from Sets of Examples. In BMVC92. https://doi.org/10.1007/978-1-4471-3201-1_2
Cootes, T. F., Taylor, C. J., Cooper, D. H., & Graham, J. (1995). Active shape models - their training and application. Computer Vision and Image Understanding. https://doi.org/10.1006/cviu.1995.1004
da Silva, L. G., da Silva Monteiro, W. R. S., de Aguiar Moreira, T. M., Rabelo, M. A. E., de Assis, E. A. C. P., & de Souza, G. T. (2021). Fractal dimension analysis as an easy computational approach to improve breast cancer histopathological diagnosis. Applied Microscopy, 51(1). https://doi.org/10.1186/s42649-021-00055-w
Davies, B. E. R., & Holloway, R. (2005). Machine Vision : Theory , Algorithms and Practicalities , Third Edition Order from Morgan Kaufmann Publishers. Pattern Recognition Letters.
Del Toro, O. A. J., Goksel, O., Menze, B., Müller, H., Langs, G., Weber, M. A., Eggel, I., Gruenberg, K., Holzer, M., Jakab, A., Kontokotsios, G., Krenn, M., Fernandez, T. S., Schaer, R., Taha, A. A., Winterstein, M., & Hanbury, A. (2014). VISCERAL -VISual concept extraction challenge in RAdioLogy: ISBI 2014 challenge organization. CEUR Workshop Proceedings.
Duan, Y., & Qin, H. (2001). Intelligent Balloon: A subdivision-based deformable model for surface reconstruction of arbitrary topology. Proceedings of the Symposium on Solid Modeling and Applications.
Dzung, L., Chenyang, X., & Prince, J. L. (1998). A survey of current methods in medical image segmentation. Department of ECE, Johns Hopkins Univ., Tech. Rep. https://doi.org/10.1039/C5AN01075F
Gage Canadian dictionary. (1983). Gage educational publishing company.
Gandhi, H. S. (2019). Rationale and options for choosing an optimal closure technique for primary midsagittal osteochondrotomy of the sternum. Part 3: Technical decision making based on the practice of patient- appropriate medicine. Critical Reviews in Biomedical Engineering. https://doi.org/10.1615/CritRevBiomedEng.2019026454
Gibson, S. F. F., & Mirtich, B. (1997). A Survey of Deformable Modeling in Computer Graphics. Merl - a Mitsubishi Electric Research Laboratory.
Goldberg, D. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman Publishing Co., Inc.
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative Adversarial Networks. http://arxiv.org/abs/1406.2661
Gower, J. C. (1975). Generalized procrustes analysis. Psychometrika. https://doi.org/10.1007/BF02291478
Haddon, J. F. (1988). Generalised threshold selection for edge detection. Pattern Recognition. https://doi.org/10.1016/0031-3203(88)90054-4
Hamameh, G., McFnemey, T., & Terzopoulos, D. (2001). Deformable organisms for automatic medical image analysis. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). https://doi.org/10.1007/3-540-45468-3_9
Hamarneh, G., Abu-Gharbieh, R., & Mcinerney, T. (2004). Medial profiles for modeling deformation and statistical analysis of shape and their use in medical image segmentation. International Journal of Shape Modeling. https://doi.org/10.1142/S0218654304000663
Hamarneh, G., McIntosh, C., McInerney, T., & Terzopoulos, D. (2009). Deformable organisms: An artificial life framework for automated medical image analysis. In Computational Intelligence in Medical Imaging: Techniques and Applications. https://doi.org/10.1201/9781420060614
Hamarneh, G., Ward, A. D., & Frank, R. (2007). Quantification and visualization of localized and intuitive shape variability using a novel medial-based shape representation. 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings. https://doi.org/10.1109/ISBI.2007.357081
Heimann, T., & Meinzer, H. P. (2009). Statistical shape models for 3D medical image segmentation: A review. Medical Image Analysis. https://doi.org/10.1016/j.media.2009.05.004
Holland, J. H. (1962). Outline for a Logical Theory of Adaptive Systems. Journal of the ACM (JACM). https://doi.org/10.1145/321127.321128
Hubel, D. H., & Wiesel, T. N. (1962). Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. The Journal of Physiology. https://doi.org/10.1113/jphysiol.1962.sp006837
Jolliffe, I. T. (2002). Principal Component Analysis, Second Edition. Encyclopedia of Statistics in Behavioral Science. https://doi.org/10.2307/1270093
Jones, M. J., & Poggio, T. (1998). Multidimensional Morphable Models: A Framework for Representing and Matching Object Classes. International Journal of Computer Vision. https://doi.org/10.1023/A:1008074226832
Kass, M., Witkin, A., & Terzopoulos, D. (1988). Snakes: Active contour models. International Journal of Computer Vision. https://doi.org/10.1007/BF00133570
Kenji Suzuki. (2017). Overview of deep learning in medical imaging. Radiol Phys Technol, 10(3), 257–273.
Kenyon, C. M., Pedley, T. J., & Higenbottam, T. W. (1991). Adaptive modeling of the human rib cage in median sternotomy. Journal of Applied Physiology, 70(5), 2287–2302. https://doi.org/10.1152/jappl.1991.70.5.2287
Khosravan, N., Mortazi, A., Wallace, M., & Bagci, U. (2019). PAN: Projective Adversarial Network for Medical Image Segmentation. http://arxiv.org/abs/1906.04378
Klette, R. (2014). Concise Computer Vision - An Introduction into Theory and Algorithms. Springer-Verlag.
Kokash, N. (2005). An introduction to heuristic algorithms. Department of Informatics and Telecommunications.
Lecun, Y., Bengio, Y., & Hinton, G. (2015a). Deep learning. In Nature. https://doi.org/10.1038/nature14539
Lecun, Y., Bengio, Y., & Hinton, G. (2015b). Deep learning. In Nature (Vol. 521, Issue 7553, pp. 436–444). Nature Publishing Group. https://doi.org/10.1038/nature14539
Li, C., Liu, J., & Fox, M. D. (2005). Segmentation of edge preserving gradient vector flow: An approach toward automatically initializing and splitting of snakes. Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005. https://doi.org/10.1109/CVPR.2005.314
Liu, X., Song, L., Liu, S., & Zhang, Y. (2021). A review of deep-learning-based medical image segmentation methods. Sustainability (Switzerland), 13(3), 1–29. https://doi.org/10.3390/su13031224
Long, J., Shelhamer, E., & Darrell, T. (2014). Fully Convolutional Networks for Semantic Segmentation. http://arxiv.org/abs/1411.4038
Luke, S. (2013). Essentials of Metaheuristics, second edition. In Optimization.
Lyon, R. F. (2006). A brief history of “pixel.” Digital Photography II. https://doi.org/10.1117/12.644941
Ma, W. Y., & Manjunath, B. S. (2000). EdgeFlow: a technique for boundary detection and image segmentation. IEEE Transactions on Image Processing. https://doi.org/10.1109/83.855433
Malladi, R., Sethian, J. A., & Vemuri, B. C. (1995). Shape Modeling with Front Propagation: A Level Set Approach. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/34.368173
Mandar, M., Sontakke, D., Meghana, M., & Kulkarni, S. (2015). Different Types of Noises in Images and Noise Removing Technique. International Journal of Advanced Technology in Engineering and Science.
Marr, D., & Hildreth, E. (1980). Theory of edge detection. Proceedings of the Royal Society of London Series B, Containing Papers of a Biological Character Royal Society (Great Britain).
Marusina, M. Y., Mochalina, A. P., Frolova, E. P., Satikov, V. I., Barchuk, A. A., Kuznetcov, V. I., Gaidukov, V. S., & Tarakanov, S. A. (2017). MRI image processing based on fractal analysis. Asian Pacific Journal of Cancer Prevention, 18(1), 51–55. https://doi.org/10.22034/APJCP.2017.18.1.51
McInerney, T., Hamarneh, G., & Shenton, M Terzopoulos, D. (2002). Deformable organisms for automatic medical image analysis. Med Image Anal., 6(3), 251–266.
McInerney, T., & Terzopoulos, D. (1996). Deformable models in medical image analysis: A survey. Medical Image Analysis. https://doi.org/10.1016/S1361-8415(96)80007-7
Mendoza, F. & Lu, R. (2015). Basics of image analysis. In B. & L. R. Park (Ed.), Hyperspectral imaging technology in food and agriculture (pp. 9–56). Springer Science + Business Media.
Milletari, F., Navab, N., & Ahmadi, S.-A. (n.d.). V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. http://promise12.grand-challenge.org/results/
Mumford, D., & Shah, J. (1989). Optimal approximations by piecewise smooth functions and associated variational problems. Communications on Pure and Applied Mathematics. https://doi.org/10.1002/cpa.3160420503
Nealen, A., Müller, M., Keiser, R., Boxerman, E., & Carlson, M. (2006). Physically based deformable models in computer graphics. Computer Graphics Forum. https://doi.org/10.1111/j.1467-8659.2006.01000.x
Olabarriaga, S. D., & Smeulders, A. W. M. (2001). Interaction in the segmentation of medical images: A survey. Medical Image Analysis. https://doi.org/10.1016/S1361-8415(00)00041-4
Osher, S., & Sethian, J. A. (1988). Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations. Journal of Computational Physics. https://doi.org/10.1016/0021-9991(88)90002-2
Otsu, N. (1979). THRESHOLD SELECTION METHOD FROM GRAY-LEVEL HISTOGRAMS. IEEE Trans Syst Man Cybern. https://doi.org/10.1109/tsmc.1979.4310076
Petrie, R. J., & Yamada, K. M. (2016). Multiple mechanisms of 3D migration: The origins of plasticity. In Current Opinion in Cell Biology. https://doi.org/10.1016/j.ceb.2016.03.025
Pham, D. L., Xu, C., & Prince, J. L. (2000). A survey in current methods in medical image processing. Annual Review of Biomedical Engineering. https://doi.org/10.1146/annurev.bioeng.2.1.315
Prats-Montalbán, J. M., de Juan, A., & Ferrer, A. (2011). Multivariate image analysis: A review with applications. In Chemometrics and Intelligent Laboratory Systems. https://doi.org/10.1016/j.chemolab.2011.03.002
Preim, B., & Botha, C. (2014). Image Analysis for Medical Visualization. In Visual Computing for Medicine. https://doi.org/10.1016/b978-0-12-415873-3.00004-3
Prewitt, J. M. S., & Mendelsohn, M. L. (1966). THE ANALYSIS OF CELL IMAGES. Annals of the New York Academy of Sciences. https://doi.org/10.1111/j.1749-6632.1965.tb11715.x
Requicha, A. A. G., & Voelcker, H. B. (1982). Solid Modeling: A Historical Summary and Contemporary Assessment. IEEE Computer Graphics and Applications. https://doi.org/10.1109/MCG.1982.1674149
Rizwan I Haque, I., & Neubert, J. (2020). Deep learning approaches to biomedical image segmentation. In Informatics in Medicine Unlocked (Vol. 18). Elsevier Ltd. https://doi.org/10.1016/j.imu.2020.100297
Rodriguez, J., Choudhury, A. A., & Dragone, B. (2011). Application of parametric solid modeling for orthopedic studies of the human spine. ASEE Annual Conference and Exposition, Conference Proceedings.
Romanycia, M. H. J., & Pelletier, F. J. (1985). What is a heuristic? Computational Intelligence. https://doi.org/10.1111/j.1467-8640.1985.tb00058.x
Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. http://arxiv.org/abs/1505.04597
Sabuncu, M. R., Balci, S. K., Golland, P., Shenton, M. E., Shenton, M. E., & Shenton, M. E. (2009). Image-Driven Population Analysis Through Mixture Modeling. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/TMI.2009.2017942
Sahoo, P. K., Soltani, S., & Wong, A. K. C. (1988). A survey of thresholding techniques. In Computer Vision, Graphics and Image Processing. https://doi.org/10.1016/0734-189X(88)90022-9
Salvi, J., Matabosch, C., Fofi, D., & Forest, J. (2007). A review of recent range image registration methods with accuracy evaluation. Image and Vision Computing. https://doi.org/10.1016/j.imavis.2006.05.012
Senthilkumaran, N., & Rajesh, R. (2009). Edge Detection Techniques for Image Segmentation – A Survey of Soft Computing Approaches. International Journal of Recent Trends in Engineering.
Sethian, J. A. (1985). Curvature and the evolution of fronts. Communications in Mathematical Physics. https://doi.org/10.1007/BF01210742
Shah, J. (1996). Common framework for curve evolution, segmentation and anisotropic diffusion. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
Shotton, J., Winn, J., Rother, C., & Criminisi, A. (2009). Texton Boost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context. International Journal of Computer Vision. https://doi.org/10.1007/s11263-007-0109-1
Sotiras, A., Davatzikos, C., & Paragios, N. (2013). Deformable medical image registration: A survey. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/TMI.2013.2265603
Sperling, G. (1970). Binocular Vision: A Physical and a Neural Theory. The American Journal of Psychology. https://doi.org/10.2307/1420686
Tam, G. K. L., Cheng, Z. Q., Lai, Y. K., Langbein, F. C., Liu, Y., Marshall, D., Martin, R. R., Sun, X. F., & Rosin, P. L. (2013). Registration of 3d point clouds and meshes: A survey from rigid to Nonrigid. In IEEE Transactions on Visualization and Computer Graphics. https://doi.org/10.1109/TVCG.2012.310
Terzopoulos, D. (1986). Regularization of Inverse Visual Problems Involving Discontinuities. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/TPAMI.1986.4767807
Timothy F. Cootes, Gareth J. Edwards, and C. J. T. (2001). Active appearance models. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 23(6), 681–685.
Vala, M., & Baxi, A. (2013). A review on Otsu image segmentation algorithm. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET).
van Kaick, O., Zhang, H., Hamarneh, G., & Cohen-Or, D. (2011). A survey on shape correspondence. Eurographics Symposium on Geometry Processing. https://doi.org/10.1111/j.1467-8659.2011.01884.x
Vuduc, R., & 634, G. (1997). Image Segmentation Using Fractal Dimension. https://www.researchgate.net/publication/2441059
Wang, J., Zhang, H., Lu, G., & Liu, Z. (2011). Rapid parametric design methods for shoe-last customization. International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-010-3144-y
Widrow, B. (1973a). The “rubber-mask” technique-I. Pattern measurement and analysis. Pattern Recognition. https://doi.org/10.1016/0031-3203(73)90042-3
Widrow, B. (1973b). The “rubber-mask” technique-II. Pattern storage and recognition. Pattern Recognition. https://doi.org/10.1016/0031-3203(73)90043-5
Xu, C., & Prince, J. L. (1997). Gradient vector flow: A new external force for snakes. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
Yamashita, R., Nishio, M., Do, R. K. G., & Togashi, K. (2018). Convolutional neural networks: an overview and application in radiology. In Insights into Imaging. https://doi.org/10.1007/s13244-018-0639-9
Yang, X. S. (2010). Engineering Optimization: An Introduction with Metaheuristic Applications. In Engineering Optimization: An Introduction with Metaheuristic Applications. https://doi.org/10.1002/9780470640425
Zhou, T., Ruan, S., & Canu, S. (2019). A review: Deep learning for medical image segmentation using multi-modality fusion. Array, 3–4, 100004. https://doi.org/10.1016/j.array.2019.100004
Zienkiewicz, O., Taylor, R., & Zhu, J. Z. (2013). The Finite Element Method: its Basis and Fundamentals: Seventh Edition. In The Finite Element Method: its Basis and Fundamentals: Seventh Edition. https://doi.org/10.1016/C2009-0-24909-9
Zitová, B., & Flusser, J. (2003). Image registration methods: A survey. Image and Vision Computing. https://doi.org/10.1016/S0262-8856(03)00137-9
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Gandhi, Harjeet Singh
This work is licensed under a Creative Commons Attribution 4.0 International License.